Although overuse of the internet has been suggested to be related to poor academic performance, the effects of internet use for education on academic performance showed conflict results in previous studies. Accordingly, the associations of school performance with internet use for study and for general purpose were explored in a large population of Korean adolescents. Cross-sectional data from the 2013 Korean Youth Risk Behaviour Web-based Survey (KYRBWS) were retrieved for 59,105 12- to 18-year-old adolescents. The associations between school performance and internet use were analysed using multinomial logistic regression with complex sampling. Days of physical activity, sex, obesity, region of residence, income level, parental education level, stress, sleep time, smoking, alcohol consumption, drug use, and total study time were recorded and adjusted for as confounders. Higher school performance was positively associated with longer internet use for study (adjusted odds ratio, AOR, of 2+ h [95% confidence interval] = 2.43 [2.10–2.82], 2.02 [1.78–2.30], 1.66 [1.46–1.89], and 1.30 [1.15–1.47] for performance groups A, B, C, and D, respectively, P < 0.001) but negatively associated with longer internet use for general purpose (AOR of 3+ h [95% confidence interval] = 0.68 [0.60–0.78], 0.85 [0.76–0.94], 0.83 [0.75–0.92], and 0.98 [0.89–1.08] for performance groups A, B, C, and D, respectively, P < 0.001). Higher school performance significantly positively correlated with internet use for study but negatively correlated with internet use for general purpose. Academic use of the internet could be a means of achieving good school performance.
Citation: Kim SY, Kim M-S, Park B, Kim J-H, Choi HG (2017) The associations between internet use time and school performance among Korean adolescents differ according to the purpose of internet use. PLoS ONE 12(4): e0174878. https://doi.org/10.1371/journal.pone.0174878
Editor: Stefano Federici, Universita degli Studi di Perugia, ITALY
Received: October 5, 2016; Accepted: March 16, 2017; Published: April 3, 2017
Copyright: © 2017 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data are available from the Korean Youth Risk Behaviour Web-based Survey (KYRBWS) for researchers who meet the criteria for access to confidential data. The restrictions prohibit the authors from making the minimal data set publicly available. These data are provided directly from KYRBWS to the individual researchers without charge. Everyone can apply for the data at the KYRBWS web site (https://yhs.cdc.go.kr/new/, tel: +82-43-719-7474,7471). These processes are intended to get consent from all researchers for the compliance of ethical guidelines. The authors did not have special access privileges to these data sets.
Funding: This work was supported in part by a research grant (NRF-2015-R1D1A1A01060860) from the National Research Foundation (NRF) of Korea and a Research Grant funded by Hallym University Sacred Heart Hospital (HURF-2016-38). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Academic performance in adolescence, which subsequently determines individuals’ final educational level, is a crucial factor that has substantial effects on adolescents’ later life. School performance could be represented by academic achievement of school grades and cognitive performance. It is determined by environmental factors such as socioeconomic levels as well as each individual characteristics including aptitude and emotional factors [1,2]. Socioeconomic inequalities, such as economic hardship, difficulty with employment, and activity limitation, have been suggested to be significantly related to education levels . In turn, the socioeconomic differences caused by income level and employment status may result in inequalities in educational levels . Therefore, the socioeconomic gap could widen due to the effects of academic performance in adolescence on later life.
The internet is a versatile means of engaging in social communication, retrieving large amounts of information, and performing leisure activities. Adequately controlled use of the internet as information and communication technologies could provide many opportunities including improvement of academic achievements [5–7]. Internet networks allow us to interact with a wide range of people worldwide. In addition, internet-based learning programs, medical education systems, and health interventions such as cognitive therapy have become widespread, generating positive outcomes [8,9]. Approximately 93% of adolescents in the U.S. have been reported to use the internet and approximately 70% of adolescents in Europe surf online for 2–4 hours per day [10,11]. It was estimated that about 99.9% of Korean adolescents use internet in daily life .
The overuse of internet, on the other hands, is uprising concern. Approximately 13.1% of adolescents were categorized as problematic internet user in the 2010 Korean Youth Risk Behavior Web-based Survey (KYRBWBS) . and approximately 21.8% of Korean adolescents have been found to overuse computers for gaming (> 3 hours per day) . Internet overuse may lead to several psychological problems, including depression, attention deficit hyperactivity disorder (ADHD), daytime sleepiness, and self-injurious behaviour [15–20]; additionally, overuse of the internet has been related to detrimental physical health conditions, such as obesity [21,22]. Problematic internet use has been suggested to be related to lower school grades . In an earlier prospective study, internet overuse and school burnout were shown to reciprocally affect each other . In addition to the distraction from schoolwork and the relative shortage of time for academic activities caused by internet overuse, socioeconomic factors such as income, parental education level, and region of residence; psychological factors including stress; and physical factors such as medication use, smoking and alcohol use may mediate the effects of internet use on school performance. These potential confounding factors should therefore be considered when exploring the associations between internet use and school performance. Therefore, there have been attempts to access the problematic internet use considering social and emotional factors in adolescents .
Although several previous studies have found that internet use has adverse effects on academic performance, they did not fully consider the purposes of internet use in their analyses [14,25]. Some researchers discriminated the purpose of internet use and investigated their influences on school performance, which showed inconsistent results [26–28]. In addition, many studies focused on extreme cases of internet use, identifying adverse psychological and behavioural outcomes due to problematic internet use [19,20,29]. However, internet provides easily accessible diverse contents, it was predicted that appropriate use of internet, in aspects of its purpose as well as using time could assist education in adolescents. To prove this hypothesis, the relationship between academic performance and internet use according to its purpose and a wide range of use durations were investigated in the Korean adolescent population.
Materials and methods
Study population and data collection
The Institutional Review Board of the Korean Centers for Disease Control and Prevention (KCDC) approved this study (2014-06EXP-02-P-A). Written informed consent was obtained from each participant prior to the survey. Because this web-based survey was performed at a school with many participants, the need to obtain informed consent from students’ parents was waived. This consent procedure was approved by the IRB of the KCDC.
This was a cross-sectional nation-wide study using data from the 2013 KYRBWS collected by the KCDC, which were analysed using statistical methods based on designed sampling and adjusted weighting. Korean adolescents in 7th through 12th grade voluntarily and anonymously completed the self-administered questionnaire. The surveys obtained data from South Korean adolescents using stratified, two-stage (schools and classes) clustered sampling based on data from the Education Ministry. Sampling was weighted by statisticians, who calculated the weights post-stratification and considered non-response rates and extreme values.
Out of a total of 72,435 participants, we excluded the following from this study: participants who did not provide information on sleep time or who slept less than 2 hours (9,128 participants); participants who did not record their height or weight (1,597 participants); and participants who did not provide the study time (2,605 participants). Finally, 59,105 participants (29,489 male and 29,616 female) aged 12 through 18 years were included in this study.
The understanding, reliability and validity of each question were investigated by the KCDC to verify the applicability of the surveys . The validity and reliability of the KYRBWS have been documented by other studies [31,32].
Possible confounders of school performance.
The time that adolescents fell asleep and woke up was measured in hours and 10-minute intervals. Duration of sleep was calculated by subtracting the time that participants fell asleep from the time that they woke up. Sleep time was classified into 4 groups: < 5.5 hours; ≥ 5.5 and < 6.5 hours; ≥ 6.5 and < 7.5 hours; and ≥ 7.5 hours. These values were selected because they were identified as the quartile points for sleep time in this study. Days of physical activity were expressed as the number of days in the past 7 days that the adolescents had exercised for more than 60 minutes at an intensity sufficient to increase their heart rate or respiration. Obesity was categorized into 4 groups according to the CDC guidelines for body mass index (BMI, kg/m2) for children and teens : obese ≥ 95th percentile; overweight ≥ 85th percentile and < 95th percentile; healthy weight ≥ 5th percentile and < 85th percentile; and underweight < 5th percentile. Region of residence was separated into 3 groups by administrative district: large cities, small cities, and rural areas. Income was grouped into 5 levels ranging from highest to lowest. Parental education level was classified into 4 groups: graduated from college or above; graduated from high school; graduated from middle school or under; and unknown or no parent. Participants who did not know the educational level of their parents or who did not have a parent were not excluded, as their exclusion could increase the number of missing values among participants with a lower income level. The self-reported stress level of participants was categorized into 5 groups: severe, moderate, mild, a little, and no stress. The participants were asked about the number of days that they had smoked in the past one month, and this value was separated into 4 groups: 0 days a month; 1–5 days a month; 6–19 days a month; and ≥ 20 days a month. The participants were also asked to provide the number of days that they had consumed alcohol in the past month, which was then categorized into 3 groups: 0 days a month; 1–5 days a month; and 6–30 days a month. Finally, participants were asked about their history of drug or substance use. Total study time was classified into 4 groups: ≤ 2 hours a day; > 2 hours and ≤ 6 hours a day; > 6 hours and ≤ 9 hours a day; and > 9 hours a day.
Independent variables: Classification of internet use.
Internet use for study was measured in hours and 10-minute increments. Mean daily internet use was calculated by adding the time spent on weekdays and the time spent on weekends, using a 5/7 weight and 2/7 weight, respectively. We separated internet use for study into 4 groups (0 hours a day [0 h]; > 0 and ≤ 1 hour a day [1 h]; > 1 and ≤ 2 hours a day [2 h]; and > 2 hours a day [2+ h]) and internet use for general purpose into 5 groups (0 h; 1 h; 2 h; > 2 and ≤ 3 hours a day [3 h]; and > 3 hours a day [3+ h]), including the 3+ h group, because only a few of the participants had used the internet for study for more than 3 hours a day.
The differences in general characteristics according to school performance were calculated using linear regression analysis with complex sampling and the Chi-square test with the Rao-Scott correction. Odds ratios (ORs) of internet use for study/ general purpose with respect to school performance were calculated using simple logistic regression analysis with complex sampling (unadjusted); multinomial logistic regression analysis with complex sampling adjusted for age, sex, obesity, region of residence, income level, education level of father, education level of mother, stress level, sleep time, days of physical activity, smoking, alcohol consumption, drug use, and total study time (model 1); and multinomial logistic regression analysis with complex sampling adjusted for the model 1 variables as well as internet use for study or other purposes (model 2).
For the subgroup analysis according to income level, adjusted ORs (AORs) of internet use for study/other purposes with respect to school performance were calculated via multinomial logistic regression analysis with complex sampling using model 2.
Two-tailed analyses were conducted, and P-values lower than 0.05 were considered to indicate significance. Additionally, 95% confidence intervals (CIs) were calculated. All results are presented as weighted values after applying the weightings recommended by the KYRBWS. The results were analysed using SPSS ver. 21.0 (IBM, Armonk, NY, USA).
In total, 11.1%, 24.4%, 28.1%, 24.7%, and 11.8% of the participants were grouped into performance group A, B, C, D, and E, respectively (Table 1). All of the considered variables including age, days of physical activity, sex, obesity, region of residence, level of income, level of parental education, stress, sleep time, smoking, alcohol consumption, drug use, and total study time significantly differed between the performance groups (P < 0.001 for each variable). Internet use times for study and general purpose showed significant differences between the school performance groups (each P < 0.001).
Compared to the lowest school performance group (E), internet use for study of 2+ h was related to higher school performance in the unadjusted model, model 1, and model 2 (AOR [95% CI] for internet use for study of 2+ h in model 2 = 2.43 [2.10–2.82], 2.02 [1.78–2.30], 1.66 [1.46–1.89], and 1.30 [1.15–1.47] for performance groups A, B, C, and D, respectively, P < 0.001). Additionally, 2 h of internet use for study was associated with higher school performance, with a slightly higher AOR (for internet use 2 h for study of 2h = 2.71 [2.40–3.07], 2.27 [2.04–2.53], 1.92 [1.73–2.14], and 1.55 [1.39–1.72] for performance groups A, B, C, and D, respectively, P < 0.001). Moreover, internet use for study of only 1 h was associated with higher school performance (AOR for internet use for study of 1 h = 2.38 [2.16–2.62], 2.12 [1.95–2.30], 1.85 [1.70–2.00], and 1.49 [1.38–1.61] for performance groups A, B, C, and D, respectively, P < 0.001) (Table 2).
Internet use for general purpose showed a negative relationship with school performance. Using the internet for general purpose for 3+ h was negatively related to higher school performance in the unadjusted model, model 1, and model 2 (AOR for internet use for general purpose of 3+ h in model 2 = 0.68 [0.60–0.78], 0.85 [0.76–0.94], 0.83 [0.75–0.92], and 0.98 [0.89–1.08] for performance groups A, B, C, and D, respectively, P < 0.001). Furthermore, 3 h of internet use for general purpose was negatively associated with higher school performance (AOR for internet use for general purpose of 3 h = 0.85 [0.74–0.98], 1.05 [0.95–1.17], 1.00 [0.90–1.11], and 1.15 [1.05–1.27] for performance groups A, B, C, and D, respectively, P < 0.001). However, relatively short use of the internet for general purpose (2 h or 1 h a day) was positively associated with school performance (AOR for internet use for general purpose of 2 h = 1.13 [1.00–1.28], 1.30 [1.17–1.44], 1.18 [1.08–1.29], and 1.17 [1.07–1.29]; AOR for internet use for general purpose of 1 h = 1.42 [1.27–1.59], 1.37 [1.25–1,49], 1.23 [1.13–1.34], and 1.15 [1.06–1.26] for performance groups A, B, C, and D, respectively, each P < 0.001). In the subgroup analysis according to income level, the associations between internet use and school performance were consistent; this relationship was strongest and most evident in the lowest income group, and it was less clear in the highest income group (Table 3). Using the internet for study for 2+ h showed high AORs in the lowest income group (3.23 [1.83–5.71], 1.40 [0.87–2.26], 1.49 [1.01–2.21], and 1.31 [0.88–1.94] for performance groups A, B, C, and D, respectively, P < 0.001) but showed lower AORs in the highest income group (1.49 [0.94–2.35], 1.29 [0.78–2.11], 0.96 [0.55–1.67], and 1.29 [0.73–2.28] for performance groups A, B, C, and D, respectively).
Internet use for study showed significant positive correlations with higher school performance, and these positive relations were highest in students who spent 2 hours a day using the internet for academic purposes. The subject with 2 hours a day of internet use for study demonstrated 2.71 times more group A school performance, compared to none internet user for study. In contrast, internet use for general purpose was associated with lower school performance. The subject with 3 or more hours a day of internet use for general purpose showed 0.68 times less group A school performance, compared to none internet user for general purpose. The beneficial correlation of internet use for study with school performance and the detrimental correlation of internet use for general purpose with school performance remained significant even after adjusting for other variables including age, physical activity, sex, obesity, region of residence, income level, parental education level, stress level, sleep time, smoking, alcohol consumption, drug use, and total study time. In the subgroup analyses by income level, the positive associations between school performance and internet use for study were weakest in the highest income group and were strongest in the lowest income group. Regardless of income level, excessive use of the internet for general purpose was related to lower school performance. The present study extended previous studies that found negative or controversial associations between academic performance and internet use by identifying a positive correlation between internet use for study and school performance based on quantitatively categorized data. The primary strength of this study is that it was based on data from a very large population. Additionally, we considered numerous factors that could be related to school performance to minimize potential confounding effects. Most importantly, these results have merits to encompass a wide range of internet use durations, from 0 hours to more than 3 hours, and to investigate the effects of internet use time on school performance, which was also classified into 5 levels. The sampling design and adjusted weighting increased the statistical power of our results.
Plausible reasons for favourable association of internet use for study with school performance
Internet use for study showed a positive association with school performance in this study. Recent studies demonstrated the beneficial effects of social media use for study on school performance , while other studies did not show significant correlation between educational use of internet and school performance [26,27]. By including every sort of internet contents for educational purpose, not restricted to social media networking, the present study revealed the discriminated, positive relation between internet use for study and school performance. Although several previous studies suggested a negative association between higher school performance and overuse or problematic internet use, they did not incorporate the reason for the internet use or considered only cases of overuse of more than 3 hours a day [9,14]. In accordance with these previous reports, our results also demonstrated that 2 h of internet use for academic purposes had a higher OR than 2 h+ of internet use for academic purposes. We believe that using the internet for excessive periods, even for studying, could impede school performance. However, there are several studies that advocate for learning using online networks [34–36]. Multiple plausible hypotheses have been provided for this approach, including connectivism, which presumes that learning phenomena involve a network of technology and socialization . In this theory, the attainment of knowledge is explained by a process of localization of information, acquisition of content, development and recollection of a recurrent pattern in the networks . The vast renewal of information and the interactive content available on the internet have the potential to promote all of these learning process. Because diverse information and opinions are available, it may be important to learn how to selectively connect these data . Internet networking can facilitate the navigation and filtering of these available data. Through online communication, students can participate and collaborate in their own learning process as well as receive feedback from instructors [35,36]. Furthermore, the completion of online academic courses and the use of collective student blogs may foster individuals’ autonomy in learning . In fact, digital learning has been increasingly applied to diverse medical education systems, showing beneficial effects [39,40].
Adverse aspects of internet use for general purpose on school performance
In contrast, internet use for general purpose had a negative correlation with school performance in our study, even after considering other possible confounding factors. The negative association between internet use for general purpose and school performance can be explained by several mental and physical changes that occur due to internet use. Intensive use of the internet leads to behavioural problems that demonstrate an aggressive, compulsive and addictive pattern [41–43]. Many internet media including blogs and games can induce compulsiveness by continually requiring users to participate and update their information in order to avoid falling behind. Thus, if appropriately used, interned media can be helpful for sustaining attention, memory and social communication skills. However, when excessively used, they may interfere with school work and sleep time and, if chronically continued, may result in an imbalance of circadian rhythms . Furthermore, internet users are continuously exposed to new messages or data, and this constant exposure can interrupt their concentration on schoolwork. Multi-tasking is often required during internet networking due to the intrusiveness of social message services, pop-up advertisements, and linked information pages. Finally, previous studies have demonstrated the occurrence of subsequent mental problems including depressive symptoms, loneliness, anxiety, and low self-esteem after excessive internet use in adolescents [44–46].
Positive aspects of internet use for general purpose on school performance
However, internet use for general purpose is not always related to poor school performance. Compared to non-use, short (1 h) use of the internet for general purpose has demonstrated positive associations with higher school performance. Several beneficial aspects of internet use could explain this positive relationship between higher school performance and internet use. Using the internet for entertainment or social networking may re-energize a student from the burden and stress imposed by schoolwork. It is well known that stress can have detrimental effects on spatial learning and memory and that an enriched environment can reverse these adverse effects . Furthermore, it is possible that using the internet might be a more favourable source of socioeconomic support for achieving better school performance than not using the internet. Although we accounted for socioeconomic factors such as income level, parental education level, and region of residence, it is plausible that other confounders of socioeconomic factors were not adjusted for in the analyses.
Our results demonstrated that the effects and purposes of internet use differed according to income level. A previous study suggested that income influenced internet use and academic performance individually but did not affect the association between using the internet and academic performance . However, their results were limited to children with low grades, indicating that greater internet use was related to better reading skills and visual-spatial skills only in youth with poor reading skills and a low grade point average . In consistent to our results, prior study showed the improvement of math test scores by using computer learning programs in low income groups . In this study, the highest income group showed an attenuated correlation between higher school performance and internet use for academic purposes. Although poor income groups may not use the internet as readily, as mentioned above, the internet is widely used due to its relatively low cost and easy accessibility compared to expensive private lessons, especially in Korea. A longitudinal study of 140 children demonstrated that greater internet use was related to higher academic performance in individuals with an annual income of 15,000 (U.S. dollars) or less [41,42]. Because individuals in the highest income groups have abundant educational resources, they may not receive additional benefits from learning through the internet. However, other income groups can more easily access the relatively cheap and qualified education available through the internet. For instance, various online educational videos can be easily accessed free of charge at any time, suggesting that learning through the internet could be a means of overcoming the rich-poor gap in education and could be particularly helpful to low income groups.
Limitations of the present study
However, the present study used a cross-sectional design, and therefore, causality could not be established. The use of anonymous self-reporting limited the potential for selection and reporting biases. School performance and time using the internet were obtained by self-report, which might limit the accuracy of the data. We used time of internet use as a parameter for the amount of internet use, without considering dependence, compulsiveness or other mental or physical problems that may be related to internet use. However, by quantifying adolescents’ internet use and categorizing the time spent in detail, we were able to investigate the possible effects of a wide range of internet use, from 0 to more than 2 hours a day, on academic performance, thus including cases other than the extreme cases of internet addiction that had previously been analysed. Moreover, previous studies support our findings that longer internet use closely correlated with adverse effects of internet use . Further studies with a prospective study design that consider additional factors potentially related to internet use or school performance will reveal the causality between internet use and school performance.
Higher school performance was significantly associated with internet use for study, with the strongest association for 2 hours of internet use for study per day. In contrast, internet use for general purpose showed a negative correlation with higher school performance. The positive association between higher school performance and internet use for study was weakest in the highest income group and strongest in the lowest income groups. The relatively low cost of the internet as a medium for providing education in Korea may explain the differential associations between internet use and school performance by income group. Therefore, internet use for academic purposes is presumed to be a versatile tool that could reduce the economic inequalities in education.
- Conceptualization: HGC.
- Formal analysis: BJP.
- Funding acquisition: HGC.
- Investigation: MSK BJP.
- Methodology: HGC.
- Validation: MSK.
- Writing – original draft: HGC SYK.
- Writing – review & editing: HGC JHK SYK.
- 1. McManus IC, Dewberry C, Nicholson S, Dowell JS. The UKCAT-12 study: educational attainment, aptitude test performance, demographic and socio-economic contextual factors as predictors of first year outcome in a cross-sectional collaborative study of 12 UK medical schools. BMC Med 2013;11:244. pmid:24229380
- 2. Van Dijk ML, De Groot RH, Van Acker F, Savelberg HH, Kirschner PA. Active commuting to school, cognitive performance, and academic achievement: an observational study in Dutch adolescents using accelerometers. BMC Public Health 2014;14:799. pmid:25096713
- 3. Cambois E, Sole-Auro A, Robine JM. Economic Hardship and Educational Differentials in Disability in 26 European Countries. J Aging Health. 2016;28(7):1214–38. pmid:27590799
- 4. Katikireddi SV, Niedzwiedz CL, Popham F. Employment status and income as potential mediators of educational inequalities in population mental health. Eur J Public Health 2016;26(5):814–6. pmid:27593454
- 5. Guan SS, Subrahmanyam K. Youth Internet use: risks and opportunities. Curr Opin Psychiatry 2009;22(4):351–6. pmid:19387347
- 6. Wittwer Jo¨rg S M. Is students' computer use at home related to their mathematical performance at school? Computers & Education 2008;50: 1558–1571.
- 7. Erdogdu Funda E E. The impact of access to ICT, student background and school/home environment on academic success of students in Turkey: An international comparative analysis. Computers & Education 2015;82: 26–49.
- 8. Wimble M. Understanding health and health-related behavior of users of internet health information. Telemed J E Health 2016 [2016 Apr 5]. http://www.ncbi.nlm.nih.gov/pubmed/27045569.
- 9. Sinclair PM, Kable A, Levett-Jones T, Booth D. The effectiveness of Internet-based e-learning on clinician behaviour and patient outcomes: A systematic review. Int J Nurs Stud 2016;57:70–81. pmid:27045566
- 10. Moreno MA, Jelenchick L, Cox E, Young H, Christakis DA. Problematic internet use among US youth: a systematic review. Arch Pediatr Adolesc Med 2011;165(9):797–805. pmid:21536950
- 11. Holstein BE, Pedersen TP, Bendtsen P, Madsen KR, Meilstrup CR, Nielsen L, et al. Perceived problems with computer gaming and internet use among adolescents: measurement tool for non-clinical survey studies. BMC Public Health BMC public health. 2014;14:361. pmid:24731270
- 12. Korean Statistical Information Service. Statistics on Internet use 2016. https://kosis.kr/eng/.
- 13. Park S. Associations of physical activity with sleep satisfaction, perceived stress, and problematic Internet use in Korean adolescents. BMC Public Health 2014;14:1143. pmid:25373558
- 14. Yang SJ, Stewart R, Lee JY, Kim JM, Kim SW, Shin IS, et al. Prevalence and correlates of problematic internet experiences and computer-using time: a two-year longitudinal study in korean school children. Psychiatry Investig 2014;11(1):24–31. pmid:24605120
- 15. Choi K, Son H, Park M, Han J, Kim K, Lee B, et al. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin Neurosci 2009;63(4):455–62. pmid:19490510
- 16. Lam LT, Peng Z, Mai J, Jing J. The association between internet addiction and self-injurious behaviour among adolescents. Inj Prev 2009;15(6):403–8. pmid:19959733
- 17. Dalbudak E, Evren C. The relationship of Internet addiction severity with Attention Deficit Hyperactivity Disorder symptoms in Turkish University students; impact of personality traits, depression and anxiety. Compr Psychiatry 2014;55(3):497–503. pmid:24374171
- 18. Yen CF, Chou WJ, Liu TL, Yang P, Hu HF. The association of Internet addiction symptoms with anxiety, depression and self-esteem among adolescents with attention-deficit/hyperactivity disorder. Compr Psychiatry 2014;55(7):1601–8. pmid:25015304
- 19. Alavi SS, Alaghemandan H, Maracy MR, Jannatifard F, Eslami M, Ferdosi M. Impact of addiction to internet on a number of psychiatric symptoms in students of isfahan universities, iran, 2010. Int J Prev Med. 2012;3(2):122–7. pmid:22347609
- 20. Jang KS, Hwang SY, Choi JY. Internet addiction and psychiatric symptoms among Korean adolescents. J Sch Health 2008;78(3):165–71. pmid:18307612
- 21. Li M, Deng Y, Ren Y, Guo S, He X. Obesity status of middle school students in Xiangtan and its relationship with Internet addiction. Obesity (Silver Spring) 2014;22(2):482–7.
- 22. Tsitsika AK, Andrie EK, Psaltopoulou T, Tzavara CK, Sergentanis TN, Ntanasis-Stathopoulos I, et al. Association between problematic internet use, socio-demographic variables and obesity among European adolescents. Eur J Public Health 2016;26(4):617–22. pmid:27114408
- 23. Salmela-Aro K, Upadyaya K, Hakkarainen K, Lonka K, Alho K. The Dark Side of Internet Use: Two Longitudinal Studies of Excessive Internet Use, Depressive Symptoms, School Burnout and Engagement Among Finnish Early and Late Adolescents. J Youth Adolesc 2017;46(2):343–57. pmid:27138172
- 24. Jelenchick LA, Eickhoff J, Christakis DA, Brown RL, Zhang C, Benson M, et al. The Problematic and Risky Internet Use Screening Scale (PRIUSS) for Adolescents and Young Adults: Scale Development and Refinement. Comput Human Behav. 2014;35.
- 25. Yang SJ, Stewart R, Kim JM, Kim SW, Shin IS, Dewey ME, et al. Differences in predictors of traditional and cyber-bullying: a 2-year longitudinal study in Korean school children. Eur Child Adolesc Psychiatry. 2013;22(5):309–18. pmid:23640732
- 26. Patricia Insúa Cerretani E BI, Paola Bully Garay. Use of information and communications technology, academic performance and psychosocial distress in university students. Computers in Human Behavior 2016;56: 119–126.
- 27. Lau WWF. Effects of social media usage and social media multitasking on the academic performance of university students. Computers in Human Behavior. 2017;68: 286–291.
- 28. Lambic D. Correlation between Facebook use for educational purposes and academic performance of students. Computers in Human Behavior. 2016;61: 313–320.
- 29. Yen JY, Ko CH, Yen CF, Wu HY, Yang MJ. The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health 2007;41(1):93–8. pmid:17577539
- 30. Reliability and validity of the Korea Youth Risk Behavior Web-based Survey Questionnaire. 2009.http://yhs.cdc.go.kr/.
- 31. Bae J, Joung H, Kim JY, Kwon KN, Kim Y, Park SW. Validity of self-reported height, weight, and body mass index of the Korea Youth Risk Behavior Web-based Survey questionnaire. J Prev Med Public Health 2010;43(5):396–402. pmid:20959710
- 32. Bae J, Joung H, Kim JY, Kwon KN, Kim YT, Park SW. Test-retest reliability of a questionnaire for the Korea Youth Risk Behavior Web-based Survey. J Prev Med Public Health. 2010;43(5):403–10. pmid:20959711
- 33. Centers for Disease Control and Prevention. About BMI for children and teens: Centers for Disease Control and Prevention. 2015. http://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bmi.html.
- 34. G JG Connectivism: a knowledge learning theory for the digital age?: Med Teach; [2016 Apr 29]. http://www.ncbi.nlm.nih.gov/pubmed/27128290.
- 35. Cheston CC, Flickinger TE, Chisolm MS. Social media use in medical education: a systematic review. Acad Med 2013;88(6):893–901. pmid:23619071
- 36. Hollinderbaumer A, Hartz T, Uckert F. Education 2.0—how has social media and Web 2.0 been integrated into medical education? A systematical literature review. GMS Z Med Ausbild 2013;30(1):Doc14. pmid:23467509
- 37. Downes EA, Connor A, Howett M. An innovative approach to enhance dermatology competencies for advanced practice nurses: service-learning with a migrant farm worker health clinic. J Nurs Educ. 2014;53(12):710–3. pmid:25494194
- 38. Lau KH. Computer-based teaching module design: principles derived from learning theories. Med Educ. 2014;48(3):247–54. pmid:24528459
- 39. Shannon SC. Osteopathic medical education in 2008: course corrections and new horizons. J Am Osteopath Assoc 2008;108(3):100–1. pmid:18391079
- 40. Sandars J, Haythornthwaite C. New horizons for e-learning in medical education: ecological and Web 2.0 perspectives. Med Teach. 2007;29(4):307–10. pmid:17786742
- 41. Ko CH, Yen JY, Liu SC, Huang CF, Yen CF. The associations between aggressive behaviors and internet addiction and online activities in adolescents. J Adolesc Health 2009;44(6):598–605. pmid:19465325
- 42. Wallace P. Internet addiction disorder and youth: There are growing concerns about compulsive online activity and that this could impede students' performance and social lives. EMBO Rep. 2014;15(1):12–6. pmid:24398129
- 43. Guertler D, Rumpf HJ, Bischof A, Kastirke N, Petersen KU, John U, et al. Assessment of problematic internet use by the Compulsive Internet Use Scale and the Internet Addiction Test: a sample of problematic and pathological gamblers. Eur Addict Res 2014;20(2):75–81. pmid:24080838
- 44. Ciarrochi J, Parker P, Sahdra B, Marshall S, Jackson C, Gloster AT, et al. The development of compulsive internet use and mental health: A four-year study of adolescence. Dev Psychol. 2016;52(2):272–83. pmid:26595355
- 45. Primack BA, Swanier B, Georgiopoulos AM, Land SR, Fine MJ. Association between media use in adolescence and depression in young adulthood: a longitudinal study. Arch Gen Psychiatry 2009;66(2):181–8. pmid:19188540
- 46. Lam LT, Peng ZW. Effect of pathological use of the internet on adolescent mental health: a prospective study. Arch Pediatr Adolesc Med 2010;164(10):901–6. pmid:20679157
- 47. Wright RL, Conrad CD. Enriched environment prevents chronic stress-induced spatial learning and memory deficits. Behav Brain Res 2008;187(1):41–7. pmid:17904657
- 48. Yang Yihua Z L, Zeng Junxia, Pang Xiaopeng, Lai Fang, Rozelle Scott. Computers and the academic performance of elementary school-aged girls in China's poor communities. Computers & Education 2013;60: 335–346.